Free Access Google.Professional-Data-Engineer.v2025-09-04.q126 Practice Test (Page 24)

Question 111

You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.

A.Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.

B.Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.

C.Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.

D.Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.

E.Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

Question 112

You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance.
You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?

A.Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.

B.Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.

C.Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance

D.Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.

Question 113

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings.
Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

A.Re-write the application to load accumulated data every 2 minutes.

B.Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

C.Convert the streaming insert code to batch load for individual messages.

D.Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Question 114

You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A.1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline
3 Schedule the DAG to run hourly

B.1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2 Create a separate DAG for each table that needs to go through the pipeline
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

C.1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators
2 Create a separate DAG for each table that needs to go through the pipeline
3 Schedule the DAGs to run hourly

D.1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline.
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

Question 115

You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non- public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?

A.Grant the Project Owner role to a service account, and run the job with it

B.Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery

C.Use a service account with the ability to read the batch files and to write to BigQuery

D.Restrict the Google Cloud Storage bucket so only you can see the files

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Question 111

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